IDEAS home Printed from https://ideas.repec.org/a/ijs/ijsrse/v11y2024i3id92.html

Crime Prediction Using Machine Learning and Deep Learning

Author

Listed:
  • P. Karthik
  • P. Jayanth
  • K. Tharun Nayak
  • K. Anil Kumar

Abstract

The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.

Suggested Citation

  • P. Karthik & P. Jayanth & K. Tharun Nayak & K. Anil Kumar, 2024. "Crime Prediction Using Machine Learning and Deep Learning," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 11(3), pages 08-15, May.
  • Handle: RePEc:ijs:ijsrse:v11:y2024:i3:id:92
    DOI: 10.32628/IJSRSET241134
    as

    Download full text from publisher

    File URL: https://ijsrset.com/home/article/view/IJSRSET241134
    File Function: Abstract page
    Download Restriction: no

    File URL: https://ijsrset.com/home/article/download/IJSRSET241134/IJSRSET241134
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/IJSRSET241134?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ijs:ijsrse:v11:y2024:i3:id:92. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (email available below). General contact details of provider: https://ijsrset.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.